# Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.

# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# and/or
# https://github.com/lessw2020/Best-Deep-Learning-Optimizers

# Ranger has been used to capture 12 records on the FastAI leaderboard.

# This version = 2020.9.4


# Credits:
# Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github:  https://github.com/Yonghongwei/Gradient-Centralization
# RAdam -->  https://github.com/LiyuanLucasLiu/RAdam
# Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.
# Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610

# summary of changes:
# 9/4/20 - updated addcmul_ signature to avoid warning.  Integrates latest changes from GC developer (he did the work for this), and verified on performance on private dataset.
# 4/11/20 - add gradient centralization option.  Set new testing benchmark for accuracy with it, toggle with use_gc flag at init.
# full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights),
# supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.
# changes 8/31/19 - fix references to *self*.N_sma_threshold;
# changed eps to 1e-5 as better default than 1e-8.

import math
import torch
from torch.optim.optimizer import Optimizer, required


def centralized_gradient(x, use_gc=True, gc_conv_only=False):
    """credit - https://github.com/Yonghongwei/Gradient-Centralization"""
    if use_gc:
        if gc_conv_only:
            if len(list(x.size())) > 3:
                x.add_(-x.mean(dim=tuple(range(1, len(list(x.size())))), keepdim=True))
        else:
            if len(list(x.size())) > 1:
                x.add_(-x.mean(dim=tuple(range(1, len(list(x.size())))), keepdim=True))
    return x


class Ranger(Optimizer):
    def __init__(
        self,
        params,
        lr=1e-3,  # lr
        alpha=0.5,
        k=6,
        N_sma_threshhold=5,  # Ranger options
        betas=(0.95, 0.999),
        eps=1e-5,
        weight_decay=0,  # Adam options
        # Gradient centralization on or off, applied to conv layers only or conv + fc layers
        use_gc=True,
        gc_conv_only=False,
        gc_loc=True,
    ):
        """
        Args:
            params:
            lr:
            alpha:
            k:
            N_sma_threshhold:
            betas:
            eps:
            weight_decay:
            use_gc:
            gc_conv_only:
            gc_loc:  `gc_loc` controls the location of GC operation for adaptive learning rate algorithms,
                including Adam, Radam, Ranger and so on. There are two locations in the algorithm
                to add GC operation for original gradient and generalized gradient, respectively.
                Generalized gradient is the variable which is directly used to update the weight.
                For adaptive learning rate algorithms, we suggest `gc_loc=False`.
                For SGD, these two locations for GC are equivalent, so we do not introduce the hyper-parameter `gc_loc`.
        """

        # parameter checks
        if not 0.0 <= alpha <= 1.0:
            raise ValueError(f"Invalid slow update rate: {alpha}")
        if not 1 <= k:
            raise ValueError(f"Invalid lookahead steps: {k}")
        if not lr > 0:
            raise ValueError(f"Invalid Learning Rate: {lr}")
        if not eps > 0:
            raise ValueError(f"Invalid eps: {eps}")

        # parameter comments:
        # beta1 (momentum) of .95 seems to work better than .90...
        # N_sma_threshold of 5 seems better in testing than 4.
        # In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.

        # prep defaults and init torch.optim base
        defaults = dict(
            lr=lr,
            alpha=alpha,
            k=k,
            step_counter=0,
            betas=betas,
            N_sma_threshhold=N_sma_threshhold,
            eps=eps,
            weight_decay=weight_decay,
        )
        super().__init__(params, defaults)

        # adjustable threshold
        self.N_sma_threshhold = N_sma_threshhold

        # look ahead params

        self.alpha = alpha
        self.k = k

        # radam buffer for state
        self.radam_buffer = [[None, None, None] for ind in range(10)]

        # gc on or off
        self.gc_loc = gc_loc
        self.use_gc = use_gc
        self.gc_conv_only = gc_conv_only
        # level of gradient centralization
        # self.gc_gradient_threshold = 3 if gc_conv_only else 1

        print(f"Ranger optimizer loaded. \nGradient Centralization usage = {self.use_gc}")
        if self.use_gc and self.gc_conv_only == False:
            print(f"GC applied to both conv and fc layers")
        elif self.use_gc and self.gc_conv_only == True:
            print(f"GC applied to conv layers only")

    def __setstate__(self, state):
        print("set state called")
        super(Ranger, self).__setstate__(state)

    def step(self, closure=None):
        loss = None
        # note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.
        # Uncomment if you need to use the actual closure...

        # if closure is not None:
        # loss = closure()

        # Evaluate averages and grad, update param tensors
        for group in self.param_groups:

            for p in group["params"]:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()

                if grad.is_sparse:
                    raise RuntimeError("Ranger optimizer does not support sparse gradients")

                p_data_fp32 = p.data.float()

                state = self.state[p]  # get state dict for this param

                if len(state) == 0:  # if first time to run...init dictionary with our desired entries
                    # if self.first_run_check==0:
                    # self.first_run_check=1
                    # print("Initializing slow buffer...should not see this at load from saved model!")
                    state["step"] = 0
                    state["exp_avg"] = torch.zeros_like(p_data_fp32)
                    state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)

                    # look ahead weight storage now in state dict
                    state["slow_buffer"] = torch.empty_like(p.data)
                    state["slow_buffer"].copy_(p.data)

                else:
                    state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32)
                    state["exp_avg_sq"] = state["exp_avg_sq"].type_as(p_data_fp32)

                # begin computations
                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                beta1, beta2 = group["betas"]

                # GC operation for Conv layers and FC layers
                # if grad.dim() > self.gc_gradient_threshold:
                #    grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True))
                if self.gc_loc:
                    grad = centralized_gradient(grad, use_gc=self.use_gc, gc_conv_only=self.gc_conv_only)

                state["step"] += 1

                # compute variance mov avg
                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)

                # compute mean moving avg
                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)

                buffered = self.radam_buffer[int(state["step"] % 10)]

                if state["step"] == buffered[0]:
                    N_sma, step_size = buffered[1], buffered[2]
                else:
                    buffered[0] = state["step"]
                    beta2_t = beta2 ** state["step"]
                    N_sma_max = 2 / (1 - beta2) - 1
                    N_sma = N_sma_max - 2 * state["step"] * beta2_t / (1 - beta2_t)
                    buffered[1] = N_sma
                    if N_sma > self.N_sma_threshhold:
                        step_size = math.sqrt(
                            (1 - beta2_t)
                            * (N_sma - 4)
                            / (N_sma_max - 4)
                            * (N_sma - 2)
                            / N_sma
                            * N_sma_max
                            / (N_sma_max - 2)
                        ) / (1 - beta1 ** state["step"])
                    else:
                        step_size = 1.0 / (1 - beta1 ** state["step"])
                    buffered[2] = step_size

                # if group['weight_decay'] != 0:
                #    p_data_fp32.add_(-group['weight_decay']
                #                     * group['lr'], p_data_fp32)

                # apply lr
                if N_sma > self.N_sma_threshhold:
                    denom = exp_avg_sq.sqrt().add_(group["eps"])
                    G_grad = exp_avg / denom
                else:
                    G_grad = exp_avg

                if group["weight_decay"] != 0:
                    G_grad.add_(p_data_fp32, alpha=group["weight_decay"])
                # GC operation
                if self.gc_loc == False:
                    G_grad = centralized_gradient(G_grad, use_gc=self.use_gc, gc_conv_only=self.gc_conv_only)

                p_data_fp32.add_(G_grad, alpha=-step_size * group["lr"])
                p.data.copy_(p_data_fp32)

                # integrated look ahead...
                # we do it at the param level instead of group level
                if state["step"] % group["k"] == 0:
                    # get access to slow param tensor
                    slow_p = state["slow_buffer"]
                    # (fast weights - slow weights) * alpha
                    slow_p.add_(p.data - slow_p, alpha=self.alpha)
                    # copy interpolated weights to RAdam param tensor
                    p.data.copy_(slow_p)

        return loss
